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AI Models 2119
- sam-hq-vit-huge↗
syscv-community
- Baichuan2-7B-Chat↗
baichuan-inc
- 📖 模型介绍/Introduction - ⚙️ 快速开始/Quick Start - 📊 Benchmark评估/Benchmark Evaluation - 👥 社区与生态/Community - 📜 声明与协议/Terms and Conditions Baichuan 2 是[百川智能]推出的新一代开源大语言模型，采用 **2.6 万亿** Tokens 的高质量语料训练，在权威的中文和英文 benchmark 上均取得同尺寸最好的效果。本次发布包含有 7B、13B 的 Base 和 Chat 版本，并提供了 Chat 版本的 4bits 量化，所有版本不仅对学术研究完全开放，开发者也仅需[邮件申请]并获得官方商用许可后，即可以免费商用。具体发布版本和下载见下表： Baichuan 2 is the new generation of large-scale open-source language models launched by Baichuan Intelligence inc.. It is trained on a high-quality corpus with 2.6 trillion tokens and has achieved the best performance in authoritative Chinese and English benchmarks of the same size. This release includes 7B and 13B versions for both Base and Chat models, along with a 4bits quantized version for the Chat model. All versions are fully open to academic research, and developers can also use them for free in commercial applications after obtaining an official commercial license through email request. The specific release versions and download links are listed in the table below:
- wav2vec2-large-xlsr-53↗
facebook
The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note that this model should be fine-tuned on a downstream task, like Automatic Speech Recognition. Check out this blog for more information. Authors: Alexis Conneau, Alexei Baevski, Ronan Collobert, Abdelrahman Mohamed, Michael Auli **Abstract** This paper presents XLSR which learns cross-lingual speech representations by pretraining a single model from the raw waveform of speech in multiple languages. We build on wav2vec 2.0 which is trained by solving a contrastive task over masked latent speech representations and jointly learns a quantization of the latents shared across languages. The resulting model is fine-tuned on labeled data and experiments show that cross-lingual pretraining significantly outperforms monolingual pretraining. On the CommonVoice benchmark, XLSR shows a relative phoneme error rate reduction of 72% compared to the best known results. On BABEL, our approach improves word error rate by 16% relative compared to a comparable system. Our approach enables a single multilingual speech recognition model which is competitive to strong individual models. Analysis shows that the latent discrete speech representations are shared across languages with increased sharing for related languages. We hope to catalyze research in low-resource speech understanding by releasing XLSR-53, a large model pretrained in 53 languages.
- MiniMax-H3-GGUF↗
joeygambino
GGUF quantizations of MiniMax-H3's 33B video+audio DiTs, quantized from the Comfy-Org bf16 repackages. Runs in ComfyUI with ComfyUI-GGUF plus a one-line architecture patch (node pack: **ComfyUI-H3-Multishot**; workflows: **MiniMax-H3-Multishot-Workflow**). > **Want smaller files?** There is a second set built from MiniMax's pruned > checkpoints, ~40% smaller at the same quant tier, in > **MiniMax-H3-curve-GGUF**. > They need **ComfyUI 0.30.0+**, and they are a different trade, not a straight > replacement - so this repo stays exactly as it is. Stay here if you are on an > older ComfyUI, or if you would rather run the larger quants. `fl2va` = text/first-last-frame to video+audio (T2V and I2V). `ref2va` = reference-conditioned generation (identity from up to 9 images, 3 videos with soundtracks, 3 voice clips).
- LTXV-13B-0.9.8-dev-GGUF↗
QuantStack
The model files can be used in ComfyUI with the ComfyUI-GGUF custom node. Place the required model(s) in the following folders: The old example workflow from last version should still work, youll just need to switch the vae and the gguf. **Example workflow** - based on the official example workflow *As this is a quantized model not a finetune, all the same restrictions/original license terms still apply.*
- Weyaxi_-_ChatAYT-Lora-Assamble-Marcoroni-gguf↗
RichardErkhov
- Sa2VA-Qwen3-VL-4B-SAM3↗
字节跳动
- RavenX-Conjecture-Qwen3-8B-MLX↗
deadbydawn101
1. Read the ConjectureBench paper (arXiv:2510.11986) — nobody had implemented it locally 2. Built the first MLX-native LEAN-FIRE pipeline on Apple Silicon 3. Fine-tuned the first conjecture generation model that exists 4. Verified Dmitry Rybin's breaking counterexample to the 30-year-old Dinitz-Garg-Goemans conjecture within hours The core insight: **conjecture generation IS prediction.** Same pipeline whether you're breaking a 30-year-old math conjecture or sizing a Polymarket position. **The killer app.** Conjecture = Prediction. Formalization = Resolution criteria. Counterexample = The trade.
- Samantha-PL-AG-Mistral-7B-v0.2-i1-GGUF↗
mradermacher
- mask2former-swin-large-ade-semantic↗
facebook
Mask2Former model trained on ADE20k semantic segmentation (large-sized version, Swin backbone). It was introduced in the paper Masked-attention Mask Transformer for Universal Image Segmentation and first released in this repository. Disclaimer: The team releasing Mask2Former did not write a model card for this model so this model card has been written by the Hugging Face team. Mask2Former addresses instance, semantic and panoptic segmentation with the same paradigm: by predicting a set of masks and corresponding labels. Hence, all 3 tasks are treated as if they were instance segmentation. Mask2Former outperforms the previous SOTA, MaskFormer both in terms of performance an efficiency by (i) replacing the pixel decoder with a more advanced multi-scale deformable attention Transformer, (ii) adopting a Transformer decoder with masked attention to boost performance without without introducing additional computation and (iii) improving training efficiency by calculating the loss on subsampled points instead of whole masks.